Recently, researchers have noticed the complexity of human emotion expression and have committed to studying the problem of multi-label emotion classification. [1]} proposed latent emotion memory (LEM) for learning latent emotion distribution and utilized bi-directional GRU to learn emotion coherence so as to realize the multi-label emotion classification of text data. [2]} employed adversarial learning and combined adversarial loss and multi-label supervised loss to achieve multi-label emotion tagging for video data. [3]} exploited Graph Neural Networks (GNNs) with heterogeneous hierarchical message passing for multi-modal multi-label emotion classification with textual, visual and acoustic modalities. However, these studies all belong to coarse-grained emotion classification which involve only 12 emotion categories at most [3]}. It is difficult for these studies to truly reflect the complex emotion categories of human. Based on this disadvantage, [5]} built a fine-grained emotion dataset with 24 types of emotions using a large collection of tweets; [6]} collected another fine-grained emotion dataset named GoEmotions with 58k English Reddit comments, which involves 27 emotion categories. Furthermore, [7]} proposed a model called Seq2Emo with a bi-directional decoder for multi-label emotion classification on GoEmotions. However, the abovementioned studies are all based on multimedia data, rather than recognizing human emotions based on physiological signals which is also known as emotion decoding. Different from these studies, for the first time, we achieve fine-grained emotion decoding from human brain activity with fMRI data, which can decode up to 80 emotion categories.
